• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.49-62
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• 2024

Reckless development of the underground by rapid urbanization causes inspection delay on replacement of existing structure and installation new facilities. However, frequent accidents occur due to deviation in construction design planned by inaccurate location information of underground structure. Meanwhile, the electrical resistivity survey, knowns as non-destructive method, is based on the difference in the electric potential of electrodes to measure the electrical resistance of ground. This method is significantly advanced with multi-electrode and deep learning for analyzing strata. However, there is no study to quantitatively assess change in electrical resistance according to geometric conditions of structures. This study evaluates changes in electrical resistance through geometric parameters of electrodes and structure. Firstly, electrical resistance numerical module is developed using generalized mesh occurring minimal errors between theoretical and numerical resistance values. Then, changes in resistances are quantitatively compared on geometric parameters including burial depth, diameter of structure, and distance electrode and structure under steady current condition. The results show that higher electrical resistance is measured for shallow depth, larger size, and proximity to the electrode. Additionally, electric potential and current density distributions are analyzed to discuss the measured electrical resistance around the terminal electrode and structure.

• The Journal of Information Systems
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• v.24 no.2
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• pp.23-52
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• 2015

Purpose This study aims to understand the role of digital knowledge in accepting the green technology. This study combined digital option theory with the second version of the Unified Theory of Acceptance and Use of Technology (UTAUT2). Contrary to other studies in which the UTAUT2 is used to explain IT adoption behavior, we look at the relationship between IT and the UTAUT2 from a new angle, incorporating an important aspect of IT, that is, digitized knowledge richness, as a determinant of the UTAUT2. Design/methodology/approach Grounded in the UTAUT2, a content analysis was conducted to investigate novel constructs dedicated to explaining green technology adoption. In this study, an amended version of the UTAUT2 specific to green technology is offered that better explains the green technology adoption behavior of consumers. Using the items identified by content analysis, we developed a questionnaire with 36 survey items. We measured all the items on a seven-point Likert-type scale. We randomly selected 402 survey respondents from a set of panel data. After a pilot study, we analyzed the main survey data by using PLS 2.0M3 and SPSS 20.0, and employed structural equation modeling to test the hypotheses. Findings The results suggest that the UTAUT2 was found to be extendable to technologies other than conventional IT. Social influence is more significant than conventional utilitarian and hedonic-based constructs such as those utilized in the UTAUT and UTAUT2 in explaining adoption behavior in the context of green technologies. The hypothesized connection between digitized knowledge richness and adoption intention was supported by the results of studies on the role of IT in formation of attitudes toward eco-friendly production. The results also indicate that digital knowledge can also encourage people to try green technology when they learn that their peers are already using the technology successfully.

• Korean Chemical Engineering Research
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• v.50 no.6
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• pp.980-987
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• 2012

Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.

• The Journal of Information Systems
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• v.25 no.1
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• pp.1-23
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• 2016

Purpose Self-congruity deals with the effect of symbolic value-expressive attributes on consumer decision and behavior, which is the theoretical foundation of the "non-utilitarian destination positioning". Functional congruity refers to utilitarian evaluation of a product or service by consumers. In addition, recent years, social network services, especially mobile social network services have created many opportunities for e-WOM communication that enables consumers to share personal consumption related information anywhere at any time. Moreover, self-construal is a hot and popular topic that has been discussed in the field of modem psychology as well as in marketing area. This study aims to examine the moderating effect of self-construal on the relationship between self-congruity, functional congruity and tourists' positive electronic word of mouth (e-WOM). Design/methodology/approach In order to verify the hypotheses, we developed a questionnaire with 32 survey items. We measured all the items on a five-point Likert-type scale. We used Sojump.com to collect questionnaire and gathered 218 responses from whom have visited Korea before. After a pilot test, we analyzed the main survey data by using SPSS 20.0 and AMOS 18.0, and employed structural equation modeling to test the hypotheses. We first estimated the measurement model for its overall fit, reliability and validity through a confirmatory factor analysis and used common method bias test to make sure that whether measures are affected by common-method variance. Then we tested the hypotheses through the structural model and used regression analysis to measure moderating effect of self-construal. Findings The results reveal that the effect of self-congruity on tourists' positive e-WOM is stronger for tourists with an independent self-construal compared with those with interdependent self-construal. Moreover, it shows that the effect of functional congruity on tourists' positive e-WOM becomes salient when tourists' self-construal is primed to be interdependent rather than independent. We expect that the results of this study can provide important implications for academic and practical perspective.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.59-78
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• 2024

In tunnel construction, the stability is evaluated by the settlement of adjacent structures and ground, but the shear strain of the ground is the main factor that determines the failure mechanism of the ground due to the tunnel excavation and the change of the operating load, and can be used to review the stability of the tunnel excavation and to calculate the reinforcement area. In this study, a twin tunnel excavation was simulated on a soft ground in an urban area through a laboratory model test to analyze the behavior of the twin tunnel excavation on the adjacent pile grouped foundation and adjacent ground. Both the displacement and the shear strain of ground were obtained using a close-range photogrammetry during laboratory model test. In addition, two-dimensional finite element numerical analysis was performed based on the model test. The results of a back-analysis showed that the maximum shear strain rate tends to decrease as the horizontal distance between the pillars of the twin tunnel and the vertical distance between the toe of the pile group and the crown of the tunnel were decreased. The impact of the second tunnel on the first tunnel and pile group was decreased as the horizontal distance between the pillars of the twin tunnel was increased. In addition, the vertical distance between the toe of the pile group and the crown of the tunnel had a relatively greater impact on the shear strain results than the horizontal distance of the pillars between the twin tunnels. According to the results of the close-range photogrammetry and numerical analysis, the settlement of adjacent pile group and adjacent ground was measured within the design criteria, but the shear strain of the ground was judged to be outside the range of small strain in all cases and required reinforcement.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.58-68
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• 2006

This work proposes a 10b 250MS/s $1.8mm^2$ 85mW 0.13um CMOS A/D Converter (ADC) for high-performance integrated systems such as next-generation DTV and WLAN simultaneously requiring low voltage, low power, and small area at high speed. The proposed 3-stage pipeline ADC minimizes chip area and power dissipation at the target resolution and sampling rate. The input SHA maintains 10b resolution with either gate-bootstrapped sampling switches or nominal CMOS sampling switches. The SHA and two MDACs based on a conventional 2-stage amplifier employ optimized trans-conductance ratios of two amplifier stages to achieve the required DC gain, bandwidth, and phase margin. The proposed signal insensitive 3-D fully symmetric capacitor layout reduces the device mismatch of two MDACs. The low-noise on-chip current and voltage references can choose optional off-chip voltage references. The prototype ADC is implemented in a 0.13um 1P8M CMOS process. The measured DNL and INL are within 0.24LSB and 0.35LSB while the ADC shows a maximum SNDR of 54dB and 48dB and a maximum SFDR of 67dB and 61dB at 200MS/s and 250MS/s, respectively. The ADC with an active die area of $1.8mm^2$ consumes 85mW at 250MS/s at a 1.2V supply.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.37-47
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• 2006

This work proposes a 10b 25MS/s $0.8mm^2$ 4.8mW 0.13um CMOS A/D Converter (ADC) for high-performance wireless communication systems such as DVB, DAB and DMB simultaneously requiring low voltage, low power, and small area. A two-stage pipeline architecture minimizes the overall chip area and power dissipation of the proposed ADC at the target resolution and sampling rate while switched-bias power reduction techniques reduce the power consumption of analog amplifiers. A low-power sample-and-hold amplifier maintains 10b resolution for input frequencies up to 60MHz based on a single-stage amplifier and nominal CMOS sampling switches using low threshold-voltage transistors. A signal insensitive 3-D fully symmetric layout reduces the capacitor and device mismatch of a multiplying D/A converter while low-noise reference currents and voltages are implemented on chip with optional off-chip voltage references. The employed down-sampling clock signal selects the sampling rate of 25MS/s or 10MS/s with a reduced power depending on applications. The prototype ADC in a 0.13um 1P8M CMOS technology demonstrates the measured DNL and INL within 0.42LSB and 0.91LSB and shows a maximum SNDR and SFDR of 56dB and 65dB at all sampling frequencies up to 2SMS/s, respectively. The ADC with an active die area if $0.8mm^2$ consumes 4.8mW at 25MS/s and 2.4mW at 10MS/s at a 1.2V supply.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.161-170
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• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.